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Category: Containers and Kubernetes

There is a very useful and interesting article by Chris Down: “In defence of swap: common misconceptions“. Chris explains what Swap is, and how it provides a backing store of anonymous pages as opposed to the actual code files, which provide backing store for file based pages.

I have no problem with the information and background knowledge he provides. This is correct and useful stuff, and I even learned a thing about what cgroups can do for me.

I do have a problem with some attitudes here. They are coming from a developers or desktop perspective, and they are not useful in a data center. At least not in mine. :-)

Load averages are an industry-critical metric – my company spends millions auto-scaling cloud instances based on them and other metrics […]

but in the article we find Matthias Urlichs saying

The point of “load average” is to arrive at a number relating how busy the system is from a human point of view.

and the article closes with Gregg quoting a comment by Peter Zijlstra in the kernel source:

This file contains the magic bits required to compute the global loadavg figure. Its a silly number but people think its important. We go through great pains to make it work on big machines and tickless kernels.

An article by Phil Calçado explains the Container Pattern “Service Mesh” and why one would want that in a really nice way.

Phil uses early networking as an example, and explains how common functionality needed in all applications was abstracted out of the application code and moved into the network stack, forming the TCP flow control layer we have in todays networking.

A similar thing is happening with other functionality that all services that do a form of remote procedure call have to have, and we are moving this into a different layer. He then gives examples of the ongoing evolution of that layer, from Finagle and Proxygen through Synapse and Nerve, Prana, Eureka and Linkerd. Envoy and the resulting Istio project of CNCF are the current result of that development, but the topic is under research, still.

We are currently experiencing a fundamental transition in the data center. In recent discussions, it occured to me how little this is understood by people in the upper layers of the stack, and how the implications are not clear to them.

In the past, three fundamentally scarce resources limited the size of the systems we could build: IOPS, bandwidth and latency. All three of them are gone to a large extent, and the systems we are discussing now are fundamentally different from what we had in “The Past™”, with “The Past” being a thing five to ten years ago.

On June, 21 there was the “Google NEXT” conference, 2017 edition, in the Kromhouthal in Amsterdam. Google had a dedicated ferry running to ship people over to the IJ north side, delivering directly at the Kromhouthal.

The event was well booked, about 1400 people showing up (3500 invites sent). That is somewhat over the capacity of Kromhouthal, actually, and it showed in the execution in several places (Toilet, Catering, and room capacity during keynotes).

The keynotes were the expected self-celebration, but if you substract that, they were mostly useful content about the future of K8s, about Googles Big Data offerings and about ML applications and how they work together with Big Data.

For the two talk slots before the lunch, I attended K8s talks. After lunch, I switched to the Big Data track. I did not attend any ML stuff, and I missed the last talk about Spanner because I got sucked into a longer private conversation.

The site mcafee.cc is not related to the corporation of the same name, but the site of one of the authors, R. Preston McAfee.

The paper looks at the utilization data from a number of public clouds, and tries to apply some dynamic price finding logic to it. The authors are surprised by the level of stability in the cloud purchase and actual usage, and try to hypothesize why is is the case. They claim that a more dynamic price finding model might help to improve yield and utilization at the same time (but in the conclusion discover why in reality that has not happened).